Modern medical imaging techniques often require the generation and analysis of a large number of 2D, 3D and 4D images from data collected by medical imaging devices. Such images can be generated using a variety of medical imaging methods or modalities such as, but not limited to, CT, MRI, CT, PET, ultrasound imaging and the like.
Three-dimensional (3D) medical images can be generated using software to combine measurement data that has been taken at different positions or angles, and to render an image from the combined data using methods such as simple surface shading or direct volume rendering. In four-dimensional (4D) imaging systems, a series of three-dimensional images obtained at different times is dynamically rendered to produce a moving 3D image, for example, a 3D ultrasound movie.
In many instances, it may be beneficial to partition a medical image into one or more segments, where each segment corresponds to, for example, a particular tissue type, organ or organ sub-component, vessel, bone or portion of bone, or other biological structure. Segmentation can be performed manually or by using one of various available automated techniques.
It is known to create atlases of the human anatomy, or particular parts of the human anatomy, which can be used in the processing or analysis of image data of a patient. Usually, particular anatomical features identified from the image data are matched to the atlas in a registration procedure. A rigid or non-rigid transformation can be applied to the image data so that the positions of particular anatomical features in the image data are aligned with positions for those features defined by the atlas. The use of such atlases and registration procedures enables, for example, direct comparisons to be performed between image data obtained from different subjects.
Known atlases may comprise, for example image data in the form of a set of pixels or voxels, and segmentation data that represents a segmentation of the image data into different anatomical regions or types of anatomical or other material (for example, particular tissue types, bone, blood). By way of example, the segmentation data can be in the form of a bit mask, but other forms of segmentation data are known. Each pixel or voxel may comprise, for example, an image intensity value or other image parameter value at a particular position within a co-ordinate system of the atlas. The image data and segmentation can be stored separately, for example as different entries in one or more databases, but linked. The image data and/or segmentation data can be referred to as an atlas data set regardless of whether they are stored separately or together.
One technique for carrying out automated segmentation is atlas-based segmentation. This technique usually uses labeled or segmented data sets as atlas data sets against which image data can be compared in order to determine the appropriate segmentation or labeling. Such techniques generally require registration of collected image data in order to align it with the atlas data sets.
Improved results can be achieved if multiple atlases are used in the atlas-based segmentation. In one possible implementation of this technique, as illustrated in overview in the flowchart of FIG. 1, multiple atlases can be registered with each other and combined into a single averaged atlas against which the image can be registered and compared in order to segment the image. In another possible implementation of atlas-based segmentation using multiple atlases, as illustrated in overview in the flowchart of FIG. 2, the image can be registered and compared against each of a plurality of atlases, thereby producing multiple segmentations of the image, wherein each segmentation is produced using a different atlas. The multiple segmentations can then be combined, e.g. using statistical combination, in order to produce the final segmented image.
In the latter case, where the image is registered to each atlas separately and the results combined, the end results are often improved when a larger number of atlases are used, which may include a larger number of anatomical variations. However, in such cases, the processing demands are often onerous and the time required to perform such calculations can become prohibitively long.